



'''simple docstring'''





import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase_									=    logging.get_logger(__name__)


lowercase_									=    {
    "vocab_file": "vocab.json",
    "tokenizer_config_file": "tokenizer_config.json",
    "merges_file": "merges.txt",
}

lowercase_									=    {
    "vocab_file": {
        "facebook/s2t-wav2vec2-large-en-de": (
            "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json"
        ),
    },
    "tokenizer_config_file": {
        "facebook/s2t-wav2vec2-large-en-de": (
            "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json"
        ),
    },
    "merges_file": {
        "facebook/s2t-wav2vec2-large-en-de": (
            "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt"
        ),
    },
}

lowercase_									=    "</w>"
lowercase_									=    "@@ "




def lowerCAmelCase    (__A):



     """simple docstring"""



     _a           =    set()
     _a           =    word[0]
     for char in word[1:]:
          pairs.add((prev_char, char))
          _a           =    char
     return pairs


# Speech2Text2 has no max input length
lowercase_									=    {"facebook/s2t-wav2vec2-large-en-de": 1_024}






class __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			Any		=			VOCAB_FILES_NAMES
 __lowerCamelCase       :			Tuple		=			PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase       :			Tuple		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase       :			int		=			['input_ids', 'attention_mask']



 def __init__(self					,  A					,  A="<s>"					,  A="<pad>"					,  A="</s>"					,  A="<unk>"					,  A=False					,  A=None					,  **A					,  )       ->	Tuple:


      """simple docstring"""



      super().__init__(
          unk_token=A					,  bos_token=A					,  eos_token=A					,  pad_token=A					,  do_lower_case=A					,  **A					,  )

      _a           =    do_lower_case

      with open(A					,  encoding='''utf-8'''			) as vocab_handle:
           _a           =    json.load(A			)
      _a           =    {v: k for k, v in self.encoder.items()}

      if merges_file is None:
           logger.info(f'''No merges files provided. {self.__class__.__name__} can only be used for decoding.'''			)

           _a           =    None
           _a           =    None
      else:
           with open(A					,  encoding='''utf-8'''			) as merges_handle:
                _a           =    merges_handle.read().split('''\n'''			)[:-1]

           _a           =    [tuple(merge.split()[:2]			) for merge in merges]
           _a           =    dict(zip(A					,  range(len(A			)			)			)			)
           _a           =    {}



 @property
 def    a__    (self			)       ->	int:


      """simple docstring"""



      return len(self.decoder			)



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      return dict(self.encoder					,  **self.added_tokens_encoder			)



 def    a__    (self					,  A			)       ->	Any:


      """simple docstring"""



      _a           =    tuple(token[:-1]			) + (token[-1] + BPE_TOKEN_MERGES,)
      if token in self.cache:
           return self.cache[token]
      _a           =    get_pairs(A			)

      if not pairs:
           return token

      while True:
           _a           =    min(A					,  key=lambda A			: self.bpe_ranks.get(A					,  float('''inf'''			)			)			)
           if bigram not in self.bpe_ranks:
                break
           _a    ,       _a           =    bigram
           _a           =    []
           _a           =    0
           while i < len(A			):
                try:
                     _a           =    word.index(A					,  A			)
                except ValueError:
                     new_word.extend(word[i:]			)
                     break
                else:
                     new_word.extend(word[i:j]			)
                     _a           =    j

                if word[i] == first and i < len(A			) - 1 and word[i + 1] == second:
                     new_word.append(first + second			)
                     i += 2
                else:
                     new_word.append(word[i]			)
                     i += 1
           _a           =    tuple(A			)
           _a           =    new_word
           if len(A			) == 1:
                break
           else:
                _a           =    get_pairs(A			)
      _a           =    ''' '''.join(A			)
      if word == "\n  " + BPE_TOKEN_MERGES:
           _a           =    '''\n''' + BPE_TOKEN_MERGES

      if word.endswith(A			):
           _a           =    word.replace(A					,  ''''''			)

      _a           =    word.replace(''' '''					,  A			)
      _a           =    word
      return word



 def    a__    (self					,  A			)       ->	List[str]:


      """simple docstring"""



      if self.bpe_ranks is None:
           raise ValueError(
               '''This tokenizer was instantiated without a `merges.txt` file, so'''
               ''' that it can only be used for decoding, not for encoding.'''
               '''Make sure to provide `merges.txt` file at instantiation to enable '''
               '''encoding.'''			)

      if self.do_lower_case:
           _a           =    text.lower()

      _a           =    text.split()

      _a           =    []
      for token in text:
           if token:
                split_tokens.extend(list(self.bpe(A			).split(''' '''			)			)			)

      return split_tokens



 def    a__    (self					,  A			)       ->	int:


      """simple docstring"""



      return self.encoder.get(A					,  self.encoder.get(self.unk_token			)			)



 def    a__    (self					,  A			)       ->	str:


      """simple docstring"""



      _a           =    self.decoder.get(A					,  self.unk_token			)
      return result



 def    a__    (self					,  A			)       ->	str:


      """simple docstring"""



      _a           =    ''' '''.join(A			)

      # make sure @@ tokens are concatenated
      _a           =    ''''''.join(string.split(A			)			)

      return string



 def    a__    (self					,  A					,  A = None			)       ->	Tuple[str]:


      """simple docstring"""



      if not os.path.isdir(A			):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
           return
      _a           =    os.path.join(
          A					,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)
      _a           =    os.path.join(
          A					,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']			)

      with open(A					,  '''w'''					,  encoding='''utf-8'''			) as f:
           f.write(json.dumps(self.encoder					,  indent=2					,  sort_keys=A					,  ensure_ascii=A			) + '''\n'''			)

      _a           =    0
      if self.bpe_ranks is None:
           return (vocab_file,)

      with open(A					,  '''w'''					,  encoding='''utf-8'''			) as writer:
           for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,  key=lambda A			: kv[1]			):
                if index != token_index:
                     logger.warning(
                         f'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'''
                         ''' Please check that the tokenizer is not corrupted!'''			)
                     _a           =    token_index
                writer.write(''' '''.join(A			) + '''\n'''			)
                index += 1

      return (vocab_file, merges_file)




"""simple docstring"""



import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


SCREAMING_SNAKE_CASE__:Dict									=			logging.get_logger(__name__)






class  snake_case__      (       snake_case_       ):


 def __init__( self    ,		*lowerCamelCase    ,		**lowerCamelCase						):
        warnings.warn(
            "The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
            " use DonutImageProcessor instead."    ,		lowerCamelCase    ,		)
        super().__init__(*lowerCamelCase    ,		**lowerCamelCase						)
